In many business environments, particularly those that involve high-volume sale and distribution of parts or supplies, proper inventory management is imperative to the successful operation of the business. In order to ensure that an on-hand inventory of parts is adequate to meet customer demand, many businesses analyze historical demand associated with their part inventories and forecast future demand for one or more selected parts. Based on the demand forecast, warehouse managers establish fixed minimum quantities of certain parts, thereby requiring that the warehouse stock a minimum quantity of the parts to comply with future demand forecast.
While the practice of stocking parts in anticipation of a future demand may ensure part availability in most cases, it often results in the buildup of unused inventory for some parts when actual demand fails to meet forecasted demand. This inventory stockpile may potentially consume valuable inventory management resources (e.g., storage space, etc.), reduce and/or waste the usable life of the part, and/or reduce profitability by expending capital for unsold and/or unused parts—capital that may have been invested in some other fashion. Thus, in order to determine an appropriate level of inventory stock to adequately respond to a future demand, while preventing the accumulation of excess inventory, methods for identifying and characterizing inventory demand may be required.
One method for identifying and forecasting supply chain demand is described in U.S. Patent Application Publication No. 2002/0169657 (“the '657 publication”) to Singh et al. The '657 publication describes a method for predicting a future demand based on buyer trend, certain seasonal effects, and/or causal factors, such as change in supply, price, etc. The demand prediction method of the '657 publication analyzes historical demand data, models the demand by adapting the historical data to a Fourier series or multiple linear regression (MLR) algorithm, and applies the model to a future time period to produce a future demand profile. The Fourier series algorithm attempts to fit historical data that displays seasonality to a periodic (e.g., sine or cosine) function. The multiple linear regression algorithm, while more complex, allows the integration of multiple independent variables associated with demand (e.g., price, weather, demographics, competitor promotions, etc.) into the forecast.
Although the method described in the '657 publication includes multiple techniques for forecasting demand associated with a supply chain, it may be inaccurate. For example, the method of the '657 patent determines seasonality based on a “peak and valley” identification approach that analyzes the amplitude of the historical demand and flags data points that do not conform to predetermined demand threshold (e.g., exceed an acceptable level of deviation from a predetermined demand range). These data points may then be used to generate a Fourier series which attempts to model the periodicity of the demand. Those skilled in the art, however, will recognize that, in certain situations, seasonal demand may, in fact, exhibit irregular behavior that may not be periodic and may not be modeled using a Fourier series. Thus, in situations where historical demand data exhibits irregular seasonal demand patterns, the method of the '657 publication may be inefficient and inaccurate.
The presently disclosed method for classifying patterns in demand variability is directed toward overcoming one or more of the problems set forth above.